Abstract:
The assessment methods for estimating the behavior of the complex mechanics of
reinforced concrete (RC) structural elements were primarily based on experimental investigation,
followed by the collective evaluation of experimental databases from the available
literature. There is still a lot of uncertainty in relation to the strength and deformability
criteria that have been derived from tests due to the differences in the experimental test
setups of the individual research studies that are being fed into the databases used to
derive predictive models. This research work focuses on structural elements that exhibit
pronounced strength degradation with plastic deformation and brittle failure characteristics.
The study’s focus is on evaluating existing models that predict the shear strength of
RC columns, which take into account important factors including the structural element’s
ductility and axial load, as well as the contributions of specific resistance mechanisms like
that of concrete, transverse, and longitudinal reinforcement. Significantly improved predictive
models are proposed herein through the implementation of machine learning (ML)
algorithms on refined datasets. Three ML models, LREGR, POLYREG-HYT, and XGBoost-
HYT-CV, were used to develop different predictive models that were able to compute the
shear strength of RC columns. According to the numerical findings, POLYREG-HYT- and
XGBoost-HYT-CV-derived models outperformed other ML models in predicting the shear
strength of rectangular RC columns with the correlation coefficient having a value R greater
than 99% and minimal errors. It was also found that the newly proposed predictive model
derived a 2-fold improvement in terms of the correlation coefficient compared to the best
available equation in international literature.
